Bibliographic Details
| Title: |
Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data: Differences, Similarities and Challenges Illustrated with Rifampicin |
| Authors: |
Lina Keutzer, Huifang You, Ali Farnoud, Joakim Nyberg, Sebastian G. Wicha, Gareth Maher-Edwards, Georgios Vlasakakis, Gita Khalili Moghaddam, Elin M. Svensson, Michael P. Menden, Ulrika S. H. Simonsson, on behalf of the UNITE4TB Consortium |
| Source: |
Pharmaceutics, Vol 14, Iss 8, p 1530 (2022) |
| Publisher Information: |
MDPI AG, 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Pharmacy and materia medica |
| Subject Terms: |
machine learning, pharmacometrics, population pharmacokinetics, rifampicin, pharmacokinetics, simulation, Pharmacy and materia medica, RS1-441 |
| More Details: |
Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- and labor-intensive. In contrast, ML models are much quicker trained, but offer less mechanistic insights. The opportunity of using ML predictions of drug PK as input for a PKPD model could strongly accelerate analysis efforts. Here exemplified by rifampicin, a widely used antibiotic, we explore the ability of different ML algorithms to predict drug PK. Based on simulated data, we trained linear regressions (LASSO), Gradient Boosting Machines, XGBoost and Random Forest to predict the plasma concentration-time series and rifampicin area under the concentration-versus-time curve from 0–24 h (AUC0–24h) after repeated dosing. XGBoost performed best for prediction of the entire PK series (R2: 0.84, root mean square error (RMSE): 6.9 mg/L, mean absolute error (MAE): 4.0 mg/L) for the scenario with the largest data size. For AUC0–24h prediction, LASSO showed the highest performance (R2: 0.97, RMSE: 29.1 h·mg/L, MAE: 18.8 h·mg/L). Increasing the number of plasma concentrations per patient (0, 2 or 6 concentrations per occasion) improved model performance. For example, for AUC0–24h prediction using LASSO, the R2 was 0.41, 0.69 and 0.97 when using predictors only (no plasma concentrations), 2 or 6 plasma concentrations per occasion as input, respectively. Run times for the ML models ranged from 1.0 s to 8 min, while the run time for the PM model was more than 3 h. Furthermore, building a PM model is more time- and labor-intensive compared with ML. ML predictions of drug PK could thus be used as input into a PKPD model, enabling time-efficient analysis. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1999-4923 |
| Relation: |
https://www.mdpi.com/1999-4923/14/8/1530; https://doaj.org/toc/1999-4923 |
| DOI: |
10.3390/pharmaceutics14081530 |
| Access URL: |
https://doaj.org/article/25a790d93aeb473e9a0152c76324f8f0 |
| Accession Number: |
edsdoj.25a790d93aeb473e9a0152c76324f8f0 |
| Database: |
Directory of Open Access Journals |
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